Citation
Manglayev, Talgat and Kizilirmak, Refik Caglar and Kho, Yau Hee and Abdul Hamid, Nor Asilah Wati and Tian, Yue
(2022)
AI based power allocation for NOMA.
Wireless Personal Communications, 124.
pp. 3253-3261.
ISSN 0929-6212; ESSN: 0929-6212
Abstract
Novel methods using artificial intelligence for downlink power allocation problem in non-orthogonal multiple access networks are presented. The proposed machine learning and deep learning based methods achieved performance close to the optimum in terms of sum capacity with significantly lower computational costs. The numerical results also demonstrated up to 120 times a boost in computation time as compared to the conventional exhaustive search approach. Furthermore, the training and testing accuracy of the deep learning model reached 0.92 and 0.93 with the loss value dropping up to 0.002.
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Official URL or Download Paper: https://link.springer.com/article/10.1007/s11277-0...
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Additional Metadata
| Item Type: | Article |
|---|---|
| Divisions: | Institute for Mathematical Research |
| DOI Number: | https://doi.org/10.1007/s11277-022-09511-6 |
| Publisher: | Springer |
| Keywords: | Non-orthogonal multiple access (NOMA); Power allocation; Artificial intelligence; Deep learning; Machine learning; 5G |
| Depositing User: | Ms. Nur Faseha Mohd Kadim |
| Date Deposited: | 17 Jul 2024 04:18 |
| Last Modified: | 17 Jul 2024 04:18 |
| Altmetrics: | http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1007/s11277-022-09511-6 |
| URI: | http://psasir.upm.edu.my/id/eprint/100156 |
| Statistic Details: | View Download Statistic |
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